Related Experiment Video

Updated: Aug 18, 2026

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Non-parametric estimation of age-related centiles over wide age ranges

H Q Pan1, H Goldstein, Q Yang

  • 1Department of Mathematics and Biostatics, Shanghai Second Medical University, People's Republic of China.

Annals of Human Biology
|November 1, 1990
PubMed

Insights

A novel method accurately estimates age-related centile curves for children up to six years old. This approach provides reliable weight centile curves for pediatric growth monitoring.

Area of Science:

  • Pediatrics
  • Biostatistics
  • Growth Monitoring

Background:

  • Accurate age-related centile curves are crucial for assessing child growth.
  • Existing methods may have limitations for wide age ranges.

Purpose of the Study:

  • To introduce a new statistical method for estimating age-related centile curves.
  • To apply this method to calculate weight centile curves for Chinese children.

Main Methods:

  • Development of a new statistical technique for centile curve estimation.
  • Application of the method to a dataset of 8995 children from birth to 6 years.

Main Results:

  • The developed method is suitable for measurements across a wide age spectrum.
  • Weight centile curves were successfully calculated for children aged 0-6 years in Shanghai.

Conclusions:

  • The new method provides a robust tool for pediatric growth assessment.
  • This research contributes valuable data for monitoring child physical development in China.

More Related Videos

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting08:13

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Related Concept Videos

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...